Vehicle Rolling Resistance Research Articles

Estimation of a battery electric vehicle output power and remaining driving range under subfreezing conditions

This manuscript proposes a hybrid machine learning approach to estimate the battery output power and remaining driving range in a battery electric vehicle (BEV). These two metrics are among the most significant factors in the market penetration of BEVs, specifically in countries with harsh winters. The proposed hybrid method comprises a recurrent dynamic network, nonlinear autoregressive with exogenous inputs (NARX), and a recursive algorithm, Kalman filter (KF). The NARX neural network utilizes the information from the weather, the vehicle's speed, and road conditions to predict the battery output power, and the remaining driving range. KF, unlike the other similar studies, is employed to account for the dynamic changes in different road conditions by providing the vehicle rolling resistance and aerodynamic drag coefficient estimates to the network. To validate the performance of the proposed method, several driving tests under different ambient conditions are conducted using a real BEV (Kia Soul 2017), and the performance of the suggested approach is compared with a conventional model-based method. The comparison between the measured battery output power and the estimated one shows that the proposed hybrid method provides more accurate estimates than a model-based approach with over 50 % of percentage change in terms of root mean squared error. Furthermore, through a systemic network optimization, it is shown that the data from two trips (out of 14 trips) are sufficient to successfully predict the real-time battery output power, energy consumption, and the remaining driving range in different weather conditions, including winter. Therefore, the proposed NARX network is particularly suitable to circumvent the range anxiety issues and hence predict the remaining range better than the traditional vehicle dynamics-based approach.

The impact of wheelchairs driving support systems on the rolling resistance coefficient

The 21stcentury is characterized by an increase in the obesity of society, including people with disabilities. The increase in the weight of people in wheelchairs requires them to have more power to drive these types of vehicles. Diseases and a varied level of disability or infirmity may lead to the inability to overcome the resistance of a vehicle driven by muscle power, such as a wheelchair. The solution to this problem are systems supporting the motion of this type of vehicles among which we can distinguish electric-manual hybrid drives that add energy to the drive system and systems that reduce the amount of force necessary for the movement of the cart lowering at the same time of its moving speed. Systems and mechanisms that have been developed for this purpose contain an increased number of cooperating elements than in a classic wheelchair, leading to an increase in rolling resistance. The article presents the methodology and test results of rolling resistance coefficients of wheelchairs with innovative systems supporting their propulsion showing that the tested auxiliary drives generate an increase in rolling resistance of vehicles for the disabled from 30 % to 75 %.

Tire Ply-Steer, Conicity and Rolling Resistance - Analytical Formulae for Accurate Assessment of Vehicle Performance during Straight Running

<div class="section abstract"><div class="htmlview paragraph">The aim of the paper is to provide simple and accurate analytical formulae describing the <i>straight motion</i> of a road vehicle. Such formulae can be used to compute either the steering torque or the additional rolling resistance induced by vehicle side-slip angle.</div><div class="htmlview paragraph">The paper introduces a revised formulation of the Handling Diagram Theory to take into account tire ply-steer, conicity and road banking. Pacejka’s Handling Diagram Theory is based on a relatively simple fully non-linear single track model. We will refer to the linear part of the Handling Diagram, since straight motion will be considered only. Both the elastokinematics of suspension system and tire characteristics are taken into account.</div><div class="htmlview paragraph">The validation of the analytical expressions has been performed both theoretically and after a subjective-objective test campaign.</div><div class="htmlview paragraph">By means of the new and unreferenced analytical formulae, practical hints are given to set to zero the steering torque during straight running. Additionally, the vehicle rolling resistance during straight motion is studied. It is found that front toe seems primarily set for reasons that are related to the steering system, other than tires. Reducing front toe could reduce energy consumption up to 1% on a WLTP mission.</div></div>

Finite element modelling of the rolling resistance due to pavement deformation

ABSTRACTRolling resistance is defined as the energy lost when the vehicle tyre rolls over a pavement surface. The pavement surface texture, roughness and deformation are shown to influence rolling resistance to varying degrees. Several recent studies have focused on determining the vehicle rolling resistance primarily based on pavement surface characteristics without quantifying the effect of pavement elastic deformations on this parameter. This paper focuses on isolating the pavement structure-induced component of the rolling resistance by performing finite element (FE) modelling on flexible and rigid pavement structures using the ABAQUS software under a moving wheel load. The structure-induced rolling resistance (SRR) was shown to be significant for flexible pavements with thin structures and weak support, and relatively insignificant for rigid pavements and thick flexible pavements at low temperature. Due to the complexity of the FE analyses, a reliable procedure was developed for predicting the SRR for in-service pavements using falling weight deflectometer data. Quantifying the effect of SRR can lead to the construction of pavement structures with lower rolling resistance, which in turn will reduce the fuel consumption and the carbon footprint of the roadway network.

Vehicle energy consumption and an environmental impact calculation model for the transportation infrastructure systems

The interaction of vehicles and tires with various surface and structural features of roadways impact rolling resistance, hence fuel consumption of vehicles. Efficient interaction of vehicles with the roadway provides new opportunities to reduce transportation systems environmental impacts. A roughness–speed impact (RSI) model was developed to quantify the energy and environmental impacts due to vehicle–pavement interaction. The RSI model accounts for the additional rolling resistance of vehicles resulting from pavement surface properties measured in terms of smoothness as well as vehicle efficiency improvements over time. The model uses vehicle-specific power as a basis to develop the analytical equations for representing vehicle–pavement interaction. The proposed analytical model offers advantages for easy integration to LCA software to evaluate GHG reduction policies. According to the model, one unit change of IRI (63.36 in/mile) results in an average increase in fuel consumption of 3% and 2%, respectively, at high and low speeds (65 and 35 mph) for passenger cars. Heavier trucks are less sensitive to IRI change; and one unit change in IRI on average results in 2% and 1% increase, respectively, in fuel consumption of heavy truck at high and low speeds. This paper presents case studies to explore effectiveness of a short-term policy goal concerning with the management of roadway surface features. According to the LCA results with different scenarios of pavement management policies, while vehicle efficiency accounts for about 27% of the potential total energy savings, potential savings from pavement roughness can be up to 7%.

Rolling resistance, vertical load and optimal number of wheels in human-powered vehicle design

Even if it makes a smaller contribution than aerodynamic drag, rolling resistance plays a non-negligible role in the efficiency of human-powered vehicles, whether they are designed for daily commuting or to set speed records. The literature, experimental evidence and models show that the rolling resistance coefficient of cycling wheels strongly depends on the supported load, suggesting that the number of wheels and the load distribution could play a role in vehicle design and in road-test data analysis. Starting with an in-depth look at the relationship between a single wheel and overall vehicle rolling resistance coefficients, an analysis is proposed and discussed with the aim of minimizing the rolling resistance of a vehicle. Finally, a parametric surface response model for rolling resistance is obtained as a function of wheel size and the number of wheels. The overall analysis overturns the popular assumption according to which ‘the more wheels, the more rolling resistance’, at least according to a strict definition of the phenomenon.

Design of an optimal active stabilizer mechanism for enhancing vehicle rolling resistance

Improving rollover and stability of the vehicles is the indispensable part of automotive research to prevent vehicle rollover and crashes. The main objective of this work is to develop active control mechanism based on fuzzy logic controller (FLC) and linear quadratic regulator (LQR) for improving vehicle path following, roll and handling performances simultaneously. 3-DOF vehicle model including yaw rate, lateral velocity (lateral dynamic) and roll angle (roll dynamic) were developed. The controller produces optimal moment to increase stability and roll margin of vehicle by receiving the steering angle as an input and vehicle variables as a feedback signal. The effectiveness of proposed controller and vehicle model were evaluated during fishhook and single lane-change maneuvers. Simulation results demonstrate that in both cases (FLC and LQR controllers) by reducing roll angle, lateral acceleration and side slip angles remain under 0.6g and 4° during maneuver, which ensures vehicle stability and handling properties. Finally, the sensitivity and robustness analysis of developed controller for varying longitudinal speeds were investigated.

Evaluation of Tire/Surfacing/Base Contact Stresses and Texture Depth

Tire rolling resistance has a major impact on vehicle fuel consumption. Rolling resistance is the loss of energy due to the interaction between the tire and the pavement surface. This interaction is a complicated combination of stresses and strains which depend on both tire and pavement related factors. These include vehicle speed, vehicle weight, tire material and type, road camber, tire inflation pressure, pavement surfacing texture etc. In this paper the relationship between pavement surface texture depth and tire/surfacing contact stress and area is investigated. Texture depth and tire/surfacing contact stress were measured for a range of tire inflation pressures on five different pavement surfaces. In the analysis the relationship between texture and the generated contact stresses as well as the contact stress between the surfacing and base layer are presented and discussed, and the anticipated effect of these relationships on the rolling resistance of vehicles on the surfacings, and subsequent vehicle fuel economy discussed.

Reducing greenhouse gas emissions through strategic management of highway pavement roughness

On-road vehicle use is responsible for about a quarter of US annual greenhouse gas (GHG) emissions. Changes in vehicles, travel behavior and fuel are likely required to meet long-term climate change mitigation goals, but may require a long time horizon to deploy. This research examines a near-term opportunity: management of pavement network roughness. Maintenance and rehabilitation treatments can make pavements smoother and reduce vehicle rolling resistance. However, these treatments require material production and equipment operation, thus requiring a life cycle perspective for benefits analysis. They must also be considered in terms of their cost-effectiveness in comparison with other alternatives for affecting climate change. This letter describes a life cycle approach to assess changes in total GHG (measured in CO2-e) emissions from strategic management of highway pavement roughness. Roughness values for triggering treatments are developed to minimize GHG considering both treatment and use phase vehicle emission. With optimal triggering for GHG minimization, annualized reductions on the California state highway network over a 10-year analysis period are calculated to be 0.82, 0.57 and 1.38 million metric tons compared with historical trigger values, recently implemented values and no strategic intervention (reactive maintenance), respectively. Abatement costs calculated using $/metric-ton CO2-e are higher than those reported for other transportation sector abatement measures, however, without considering all benefits associated with pavement smoothness, such as vehicle life and maintenance, or the time needed for deployment.

A New Computational Model about Vehicle’s Sliding Resistance Coefficients

Vehicle’s sliding resistance mainly includes rolling resistance, air drag resistance and friction within the transmission, wheel bearings and other related components. Among those, rolling resistance and air drag always exist whenever vehicle is running, so they have great influence on vehicle’s dynamic performance and fuel economy. Therefore, it is important to determine vehicle’s rolling resistance coefficient and air drag coefficient quickly and accurately in order to operate vehicle properly and reduce the vehicle’s fuel consumption. Combining theoretical analysis with experimental verification, calculation model based on road coasting test was given by means of least squares principle. And through which vehicle rolling resistance coefficient and air drag coefficient were determined easily. Then by using the test data from some Minibus, the vehicle's rolling resistance coefficient and air drag coefficient are calculated according to established model. The computation result shows that rolling resistance coefficient is a linear function of the speed and the air drag coefficient is constant. Finally, the analysis shows that the calculation model is simple, precise and useful.

Thin spring steel band wheel with a rubber tread, a development proposal with potential to reduce fuel consumption on some road vehicles

A reduction in vehicle rolling resistance is one of the goals of a partially developed vehicle suspension, called a band wheel or a track wheel. Instead of a pneumatic tyre, a band wheel uses an oval thin spring steel band with a very low hysteresis energy loss. The lower segment of the oval band acts like a leaf spring suspension. The current status of this suspension development is summarized. The rolling loss of the band wheel is calculated and estimated. New details describe the steps in producing and forming the critical thin steel spring-loaded band structure. Bonding of a rubber tread to the steel requires close attention to tread design to minimize its rolling resistance. A development project is needed to produce highly uniform and durable spring bands to demonstrate and measure the band wheel rolling resistance.

Velocity effect of vehicle rolling resistance in sand

Vehicle rolling resistance is important to vehicle and aircraft ground movement in loose soil and sand conditions, especially in military operations. Off-road rolling resistance measurements have been taken at relatively low velocities, 2.1–4.6 m/s (meters per second) (7–15 ft/s) (4–9 knots), which has precluded studying any potential velocity effect on rolling resistance. With the emergence of aircraft designed to be used on unimproved soil runways and the military’s proposed new vehicles, especially the lighter-weight, 23–500-kg (50–1100-lbs) robotics, capable of high-velocity off-road movement, it is important that the rolling resistance be measured at velocities closer to the expected operating conditions. This paper presents data from rubber tire rolling resistance measurements in three depths of uniform-density dry sand at velocities from 2.1 up to 18 m/s (7–58 ft/s) (4–34 knots) and three load ranges from 4534 to 10,266 N (1020–2308 lbs). In dry sand the rolling resistance increases with velocity until the tire starts to “plane,” where it levels off or decreases and it is modified by load. Limited past work in this area is presented along with some existing analytical descriptions of rolling resistance versus velocity. Limited agreement between the published equations and the test data, along with contradictory published results, indicate that additional work on this topic is needed.

Rut depth, soil compaction and rolling resistance when using bogie tracks

There are few studies of rolling resistance for bogie tracks on forestry machines. The aim of this study was to compare the effects of wheels and two types of bogie tracks on rut formation, cone index, and vehicle rolling resistance on some typical forest soils in Sweden. In an experiment, two types of tracks were put on a trailer with a bogie with hydraulic extension on the pulling bar giving the trailer repeatable travelling speed. Loads of 0 and 9.9 Mg were used on the trailer. The main results of this study are: Compared to rather wide and soft tires, tracks on the bogie reduced rut depth by up to 40% and cone index in the ruts by about 10%, although the tracks increased the mass on the trailer by 10–12%. The relative rolling resistance coefficient was not higher for tracks than for wheels. Further studies should be conducted to show the effect of track tension on rolling resistance and flotation and of the effects of tracks on heavy vehicles on subsoil compaction.

Calculation of vehicle aerodynamic drag coefficients from velocity fitting of coastdown data

Analysis of coastdown data is a useful tool for the extraction of rolling resistance and aerodynamic drag coefficients of a road vehicle. Cenek and Shaw [1], from Central Laboratories, Works Consultancy Service, New Zealand, published velocity-time profiles incorporating continuous on-board anemometry and road slope measurement. This paper discusses the mathematical and statistical techniques used for the extraction of parameter values and their confidence limits from these profiles. The vehicle rolling resistance F R and aerodynamic drag F D are assumed to have the respective forms at speed v F R = a 0 + a n v n F D = a D[ V cos Ψ] 2 where a 0, a n , and a D are functions of the relative vehicle speed V and yaw angle Ψ. Optimal values of the related rolling resistance and drag coefficients for a given data set are found from a non-linear least-squares fitting procedure. In the case n = 1, confidence limits on the aerodynamic drag coefficient, as well as a measure of the suitability of the model, are obtained. Also considered is the sensitivity of the coefficients to variations in the input data. Overall confidence limits on the coefficients may be reduced by averaging values over many separate runs. On the question of the value of n, ref. 1 presents experimental evidence, based on driving torque data, for taking n=2, a value used in road-load calculations, and the implication of this from the coastdown analysis point of view is discussed.

On the value for vehicle rolling resistance: Kurzel, I.A.Avtom.Prom., No.6, June 1963 (Russian)

On the value for vehicle rolling resistance: Kurzel, I.A.Avtom.Prom., No.6, June 1963 (Russian)